Smoothing time series

Time series decomposition allows us to extract distinct components from time series data. The smoothing technique enables us to forecast the future values of time series data. In this recipe, we introduce how to use the HoltWinters function to smooth time series data.

Getting ready

Ensure you have completed the previous recipe by generating a time series object and storing it in two variables: m and m_ts.

How to do it…

Please perform the following steps to smooth time series data:

1. First, use HoltWinters to perform Winters exponential smoothing:

   > m.pre <- HoltWinters(m)
   > m.pre
   Holt-Winters exponential smoothing with trend and additive seasonal component.
   
   Call:
   HoltWinters(x = m)
   
   Smoothing parameters:
    alpha: 0.8223689
    beta : 0.06468208
    gamma: 1
   
   Coefficients:
            [,1]
   a  1964.30088
   b    32.33727
   s1  -51.47814
   s2   17.84420
   s3  146.26704
   s4   70.69912
   

2. Plot the smoothing result:

   > plot(m.pre)
   

   

   Figure 9: A time series plot with Winters exponential smoothed...